GenAI Engineer

Design and develop AI/ML and Generative AI solutions for banking use cases including fraud detection, risk modeling, and customer analytics.

• Build, fine-tune, and deploy ML models and LLMs for credit scoring, AML, and automation

• Implement RAG-based GenAI applications using internal banking data

• Develop scalable data pipelines for training, validation, and real-time inference

• Collaborate with risk, compliance, finance, and business teams for AI solutions

• Ensure regulatory compliance and AI governance standards

• Implement data security, privacy, and access control mechanisms

• Integrate AI models into production using APIs and microservices

• Apply prompt engineering and model optimization techniques

• Monitor model performance, drift detection, and continuous improvement

• Develop explainable AI (XAI) for transparent decision-making

• Optimize cost, latency, and scalability of AI systems

• Troubleshoot AI/ML system issues across data and deployment layers

• Write efficient Python code using AI frameworks

• Follow MLOps best practices (CI/CD, automated deployment)

• Ensure responsible AI practices (bias, fairness, ethics)

• Mentor teams and contribute to enterprise AI platforms.

• Languages: Python

• AI/ML & GenAI: Machine Learning, Deep Learning, LLMs, Prompt Engineering, Fine-tuning

• Frameworks: TensorFlow, PyTorch

• GenAI Tools: LangChain, LlamaIndex

• Vector DB: Pinecone, FAISS

• Cloud Technologies: AWS / Azure / GCP

• Data Pipelines: ETL/ELT, Real-time & Batch Processing

• Integration: APIs, Microservices

• Concepts: RAG Architecture, XAI, Model Optimization

• Methodologies: Agile/Scrum, MLOps (CI/CD, Model Versioning, Deployment)

• Compliance: Banking regulations (SR 11-7, GDPR), Model Risk Management

• Soft Skills: Strong communication, stakeholder management, and analytical thinking

Do you have experience in Stakeholder relationship building?

(part of Tata group) 3.93.9 out of 5 stars Wilmington, DE $100,000 - $110,000 a year